The Progress of Glucose Monitoring-A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors

Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions

Continuous monitoring of glucose levels is important for diabetes management and prevention. While traditional glucose monitoring methods are often invasive and expensive, recent approaches using machine learning (ML) models have explored non-invasive alternatives-but many still depend on manually logged food intake and activity, which is burdensome and impractical for everyday use. In this study, we propose a novel approach that eliminates the need for manual input by utilizing only passively collected, automatically recorded multi-modal data from non-invasive wearable sensors. This enables practical and continuous glucose prediction in real-world, free-living environments. We used the BIG IDEAs Lab Glycemic Variability and Wearable Device Data (BIGIDEAs) dataset, which includes approximately 26,000 CGM readings, simultaneous ly collected wearable data, and demographic information. A total of 236 features encompassing physiological, behavioral, circadian, and demographic factors were constructed. Feature selection was conducted using random-forest-based importance analysis to select the most relevant features for model training. We evaluated the effectiveness of various ML regression techniques, including linear regression, ridge regression, random forest regression, and XGBoost regression, in terms of prediction and clinical accuracy. Biological sex, circadian rhythm, behavioral features, and tonic features of electrodermal activity (EDA) emerged as key predictors of glucose levels. Tree-based models outperformed linear models in both prediction and clinical accuracy. The XGBoost (XR) model performed best, achieving an R-squared of 0.73, an RMSE of 11.9 mg/dL, an NRMSE of 0.52 mg/dL, a MARD of 7.1%, and 99.4% of predictions falling within Zones A and B of the Clarke Error Grid. This study demonstrates the potential of combining feature engineering and tree-based ML regression techniques for continuous glucose monitoring using wearable sensors.

Continuous glucose monitoring: Minimally and non-invasive technologies

This paper highlights technological advancements in non-invasive blood glucose monitoring against the backdrop of increasing global prevalence of diabetes. Traditional monitoring methods, primarily invasive methods face limitations in providing continuous glucose level data, which is essential for effective and timely diagnosis of disease stage and for determining the optimal therapeutic strategy. Recent non-invasive technologies encompass optical, acoustic, electromagnetic, and chemical approaches. These technologies exploit the intrinsic properties of glucose, such as its optical absorption coefficients, to offer promising avenues for less intrusive blood glucose monitoring. Despite these advancements, challenges in achieving high accuracy persist due to interference from substances like water and other blood components. This underlines the need for sophisticated algorithms and sensor designs for accurate glucose estimation. Further research is required to integrate various sensing techniques and advanced data processing to enhance accuracy and user-friendliness. In conclusion, while significant progress has been made, developing a reliable, convenient, and accessible method for non-invasive glucose monitoring is crucial for transforming diabetes management and improving patients' quality of life.

Noninvasive blood glucose monitoring using a dual band microwave sensor with machine learning

The potential for continuous non-invasive blood glucose monitoring has attracted a lot of interest in the field of medical diagnostics. This paper provides a new shape of a dual-band bandpass filter (DBBPF) acting as a microwave transmission line sensor for continuous non-invasive blood glucose monitoring operating at 2.45 and 5.2 GHz. The proposed system uses the interaction between biological tissues and microwave signals to correctly assess blood glucose levels. The proposed dual-band bandpass filter (DBBPF), comprises three split ring resonator (SRR) cells with different dimensions. It is designed to operate as a sensor with improved sensitivity, compact dimensions, and a high-quality factor. It also ensures a reasonable bandwidth for lower and higher bands of 8.6 and 2%, respectively in the industrial, scientific, medical band, and the wireless local area network (ISM and WLAN) Bands. A dual-band filter enhances measurement sensitivity and specificity by targeting specific frequency ranges where glucose exhibits distinctive dielectric responses, thereby providing redundant data points for accurate glucose level determination. Glucose concentrations can be evaluated by measuring the changes in the dielectric properties of blood by sending microwave waves through the body and assessing the collected S-parameter signals. The measurement parameters encompass the reflection, phase, magnitude, as well as transmission parameters. This yields multiple evaluations of the glucose-induced alterations. Simulations are validated through laboratory measurements incorporating a phantom finger model for capturing realistic outcomes. Machine learning models are employed to analyze the sensor data, improving the accuracy of diabetes detection. Simulations are validated through laboratory measurements incorporating a phantom finger model for capturing realistic outcomes. A Cole-Cole model, implemented using MATLAB, is utilized for the phantom finger model. The main results reveal the success of the proposed transmission-based microwave glucose sensing, with a remarkable sensitivity of 1 ~ 1.5 dB for glucose level change up to 200 mg/dL.

Microwave Antenna Sensing for Glucose Monitoring in a Vein Model Mimicking Human Physiology

Non-invasive glucose monitoring has become a critical area of research for diabetes management, offering a less intrusive and more patient-friendly alternative to traditional methods such as finger-prick tests. This study presents a novel approach using a semi-solid tissue-mimicking phantom designed to replicate the dielectric properties of human skin and blood vessels. The phantom was simplified to focus solely on the skin layer, with embedded channels representing veins to achieve realistic glucose monitoring conditions. These channels were filled with D-(+)-Glucose solutions at varying concentrations (60 mg/dL to 200 mg/dL) to simulate physiological changes in blood glucose levels. A miniature patch antenna optimized to operate at 14 GHz with a penetration depth of approximately 1.5 mm was designed and fabricated. The antenna was tested in direct contact with the skin phantom, allowing for precise measurements of the changes in glucose concentration without interference from deeper tissue layers. Simulations and experiments demonstrated the antenna's sensitivity to variations in glucose concentration, as evidenced by measurable shifts in the dielectric properties of the phantom. Importantly, the system enabled stationary measurements by injecting glucose solutions into the same blood vessels, eliminating the need to reposition the sensor while ensuring reliable and repeatable results. This work highlights the importance of shallow penetration depth in targeting close vessels for noninvasive glucose monitoring, and emphasizes the potential of microwave-based sensing systems as a practical solution for continuous glucose management.

A low-power approach to optical glucose sensing via polarisation switching

High-precision polarimetry is crucial for sensing and imaging applications, particularly for glucose monitoring within the physiological range of 50 to 400 mg/dl. Traditional approaches often rely on polarisation modulation using magneto-optic or liquid crystal modulators, which require high voltages or currents, limiting their practicality for wearable or implantable devices. In this work, we propose a polarisation-switching technique that alternates between two discrete polarisation states, offering a low-power alternative with miniaturisation potential. Using this method, we achieved a Mean Absolute Relative Difference of 7.7% and a Standard Error of Prediction of 9.6 mg/dl across the physiological glucose range, comparable to commercial continuous glucose monitors. Our approach demonstrates a limit of detection of approximately 40 mg/dl, with measurements performed in phosphate-buffered saline spiked with glucose. This work establishes polarisation switching as a viable alternative for glucose sensing, providing a foundation for future development of wearable and implantable glucose monitoring systems. By eliminating power-intensive components, our approach addresses key limitations of traditional polarimetric methods, paving the way for more accessible and energy-efficient diabetes management technologies.

Metal Nanocomposites as Biosensors for Biological Fluids Analysis

Metal nanocomposites are rapidly emerging as a powerful platform for biosensing applications, particularly in the analysis of biological fluids. This review paper examines the recent advancements in the development and application of metal nanocomposites as biosensors for detecting various analytes in complex biological matrices such as blood, serum, urine, and saliva. We discuss the unique physicochemical properties of metal nanocomposites, including their high surface area, enhanced conductivity, and tunable optical and electrochemical characteristics, which contribute to their superior sensing capabilities. The review will cover various fabrication techniques, focusing on their impact on the sensitivity, selectivity, and stability of the resulting biosensors. Furthermore, we will analyze the diverse applications of these biosensors in the detection of disease biomarkers, environmental toxins, and therapeutic drugs within biological fluids. Finally, we will address the current challenges and future perspectives of this field, highlighting the potential for improved diagnostic tools and personalized medicine through the continued development of advanced metal nanocomposite-based biosensors.

Blood Glucose Monitoring Biosensor Based on Multiband Split-Ring Resonator Monopole Antenna

This paper introduces a novel-shaped, compact, multiband monopole antenna sensor incorporating an irregular curved split-ring resonator (SRR) design for non-invasive, continuous monitoring of human blood glucose levels (BGL). The sensor operates at multiple resonance frequencies: 0.94, 1.5, 3, 4.6, and 6.3 GHz, achieving coefficient reflection impedance bandwidths ≤ -10 dB of 4%, 1%, 3.5%, 65%, and 50%, respectively. Additionally, novel shapes of two SRR metamaterial cells create notches at 1.7 GHz and 4.4 GHz. The antenna is fabricated on an economical FR4 substrate with compact dimensions of 35 × 50 × 1.6 mm3. The sensor's performance is evaluated using 3D electromagnetic software, incorporating a human finger phantom model and applying the Cole-Cole model to mimic the blood layer's sensitivity to blood glucose variations. The phantom model is positioned at different angles relative to the biosensor to detect frequency shifts corresponding to different glucose levels. Experimental validation involves placing a real human finger around the sensor to measure resonant frequency, magnitude, and phase changes. The fabricated sensor demonstrates a superior sensitivity of 24 MHz/mg/dL effectiveness compared to existing methods. This emphasizes its potential for practical, non-invasive glucose monitoring applications.

Enhancing Nonenzymatic Glucose Detection Through Cobalt-Substituted Hafnia

Engineered defect chemistry in ultrathin (≈5 nm) hafnia through substitutional cobalt (HCO) is investigated for selective glucose sensing. Thin films of HCO, grown using chemical solution deposition (CSD)-traditionally used to grow thick films-on silicon, show significant glucose sensing activity and undergo monoclinic to orthorhombic phase transformation. The presence of multivalent cobalt in hafnia, with oxygen vacancies in proximity, selectively oxidizes glucose with minimal interference from ascorbic acid, dopamine, and uric acid. Theoretical investigations reveal that these oxygen vacancies create a shallow donor level that significantly enhances electrocatalytic activity by promoting charge transfer to the conduction band. This results in considerable selectivity, repeatability, and reproducibility in sensing characteristics. These findings highlight the technological importance of using CSD for thin films, paving the way for ultrathin CSD-processed HCOs as potential candidates for selective glucose sensing applications.

Wearable textile sensors for continuous glucose monitoring

Diabetes and cardiovascular disease are interlinked chronic conditions that necessitate continuous and precise monitoring of physiological and environmental parameters to prevent complications. Non-invasive monitoring technologies have garnered significant interest due to their potential to alleviate the current burden of diabetes and cardiovascular disease management. However, these technologies face limitations in accuracy and reliability due to interferences from physiological and environmental factors. This review investigates electronic textiles (e-textiles) that integrate biomedical sensors into wearable fabrics that can enable a multimodal platform for non-invasive continuous glucose monitoring (CGM). Current advancements in e-textiles show the potential of four key methods for glucose monitoring: optical, biochemical, biomechanical, and thermal sensing techniques. Biochemical sensing through sweat-based glucose detection has demonstrated potential for accurate and non-invasive monitoring but still faces numerous challenges. While optical, biomechanical and thermal sensing are less explored in e-textiles, they offer additional physiological and environmental insights that can improve the precision of glucose readings by providing cross-validation of data. This review proposes that integrating multiple sensing modalities into a single multimodal e-textile wearable can address the accuracy and reliability challenges by providing cross-validation of data. The development of such multimodal e-textiles has the potential to revolutionise diabetes and cardiovascular disease management by providing continuous, accurate, and holistic monitoring in real-time, which could significantly improve patient outcomes and quality of life. Further research and development are crucial to fully realise the potential of these integrated systems in clinical and everyday settings.

Exploring the impact of advanced glycation end products on diabetic salivary gland dysfunctions

The role of Advanced Glycation End Products (AGEs) in the pathophysiology of salivary gland dysfunction in diabetes has not been fully addressed. In this work, we discuss the pathophysiological mechanisms of salivary gland dysfunctions in diabetes, focusing on the role of AGEs. Hyperglycemia induces the generation and accumulation of AGEs, induces oxidative stress, and activates the receptor for AGEs (RAGE), with detrimental effects on the salivary glands and the submandibular autonomic innervation. Structural and ultrastructural alterations have been described in the three major salivary glands, and hypo-salivation development has been linked to early autonomic neuropathy. Poor metabolic control aggravates the salivary flow rate via injury to the autonomic nerve fiber bundles or direct damage to the secretory acinar cells of the glands. Chronic hyperglycemia, the most crucial feature of diabetes, leads to the generation and accumulation of advanced glycation end products (AGEs). The interest in the role of AGEs in the pathogenesis of diabetic complications has grown exponentially, and AGEs have been implicated as a primary culprit in the pathophysiology of diabetes and its various complications, including neuropathy, nephropathy, retinopathy, vasculopathy, and cardiomyopathy.

Calibration and performance of a Raman-based device for non-invasive glucose monitoring in type 2 diabetes

Raman spectroscopy has been demonstrated as a viable technique for non-invasive glucose monitoring (NIGM). However, its clinical utility is limited by an extended calibration period lasting several weeks. In this study, we address this limitation by employing a pre-trained calibration model, which is individualized through a brief calibration phase consisting of 10 measurements. The performance of the Raman-based NIGM device was evaluated in a clinical trial involving 50 individuals with type 2 diabetes over a 2-day study period. The protocol included a 4-h calibration phase on the first day, followed by validation phases of 4 h and 8 h on days 1 and 2, respectively. NIGM glucose readings were compared with capillary blood glucose measurements, with glucose fluctuations induced by standardized meal challenges. The numerical and clinical accuracy of the NIGM device was evaluated on 1918 paired points and expressed by mean absolute relative difference of 12.8% (95% CI 12.4, 13.2) and consensus error grid analysis showing 100% of NIGM readings in zones A and B. These results highlight the ability to reliably track blood glucose levels in people with type 2 diabetes. The successful introduction of a practical calibration scheme underlines Raman spectroscopy as a promising technology for NIGM and constitutes an important step towards factory calibration.

A Platform for the Glucose Biosensor Based on Dendritic Gold Nanostructures and Polyaniline-Gold Nanoparticles Nanocomposite

Diabetes mellitus is a pathological condition that requires continuous measurement of glucose concentration in human blood. In this study, two enzymatic mediator-free glucose biosensors based on premodified graphite rod (GR) electrodes were developed and compared. GR electrode modified with electrochemically synthesized dendritic gold nanostructures (DGNS), a cystamine (Cys) self-assembled monolayer (SAM), and glucose oxidase (GOx) (GR/DGNS/Cys/GOx) and GR electrode modified with DGNS, Cys SAM, enzymatically obtained polyaniline (PANI) nanocomposites with embedded 6 nm gold nanoparticles (AuNPs) and GOx (GR/DGNS/Cys/PANI-AuNPs-GOx/GOx) were investigated electrochemically. Biosensors based on GR/DGNS/Cys/GOx and GR/DGNS/Cys/PANI-AuNPs-GOx/GOx electrodes were characterized by a linear range (LR) of up to 1.0 mM of glucose, storage stability of over 71 days, sensitivity of 93.7 and 72.0 μA/(mM cm2), limit of detection (LOD) of 0.027 and 0.034 mM, reproducibility of 13.6 and 9.03%, and repeatability of 8.96 and 8.01%, respectively. The GR/DGNS/Cys/PANI-AuNPs-GOx/GOx electrode was proposed as more favorable for glucose concentration determination in serum due to its better stability and resistance to interfering electrochemically active species. The technological solutions presented in this paper are expected to enable the development of innovative mediator-free enzymatic glucose biosensors, offering advantages for clinical assays, particularly for controlling blood glucose concentration in individuals with diabetes.

A Deep Sparse Capsule Network for Non-Invasive Blood Glucose Level Estimation Using a PPG Sensor

Diabetes, a chronic medical condition, affects millions of people worldwide and requires consistent monitoring of blood glucose levels (BGLs). Traditional invasive methods for BGL monitoring can be challenging and painful for patients. This study introduces a non-invasive, deep learning (DL)-based approach to estimate BGL using photoplethysmography (PPG) signals. Specifically, a Deep Sparse Capsule Network (DSCNet) model is proposed to provide accurate and robust BGL monitoring. The proposed model's workflow includes data collection, preprocessing, feature extraction, and predictions. A hardware module was designed using a PPG sensor and Raspberry Pi to collect patient data. In preprocessing, a Savitzky-Golay filter and moving average filter were applied to remove noise and preserve pulse form and high-frequency components. The DSCNet model was then applied to predict the sugar level. Two models were developed for prediction: a baseline model, DSCNet, and an enhanced model, DSCNet with self-attention. DSCNet's performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Relative Difference (MARD), and coefficient of determination (R2), yielding values of 3.022, 0.05, 0.058, 0.062, 10.81, and 0.98, respectively.

The Usefulness of the Glucose Management Indicator in Evaluating the Quality of Glycemic Control in Patients with Type 1 Diabetes Using Continuous Glucose Monitoring Sensors: A Cross-Sectional, Multicenter Study

The Glucose Management Indicator (GMI) is a biomarker of glycemic control which estimates hemoglobin A1c (HbA1c) based on the average glycemia recorded by continuous glucose monitoring sensors (CGMS). The GMI provides an immediate overview of the patient's glycemic control, but it might be biased by the patient's sensor wear adherence or by the sensor's reading errors. This study aims to evaluate the GMI's performance in the assessment of glycemic control and to identify the factors leading to erroneous estimates. In this study, 147 patients with type 1 diabetes, users of CGMS, were enrolled. Their GMI was extracted from the sensor's report and HbA1c measured at certified laboratories. The median GMI value overestimated the HbA1c by 0.1 percentage points (p = 0.007). The measurements had good reliability, demonstrated by a Cronbach's alpha index of 0.74, an inter-item correlation coefficient of 0.683 and an inter-item covariance between HbA1c and GMI of 0.813. The HbA1c and the difference between GMI and HbA1c were reversely associated (Spearman's r = -0.707; p < 0.001). The GMI is a reliable tool in evaluating glycemic control in patients with diabetes. It tends to underestimate the HbA1c in patients with high HbA1c values, while it tends to overestimate the HbA1c in patients with low HbA1c.

In vivo Raman spectroscopy for non-invasive transcutaneous glucose monitoring on animal models and human subjects

Non-invasive glucose monitoring represents a significant advancement in diabetes management and treatment as non-painful alternatives than finger-sticks tests. After developing an integrated Raman spectral system with a 785 nm laser, this study systematically explores the application of in vivo Raman spectroscopy for quantitative, noninvasive glucose monitoring. In addition to observing characteristic glucose spectral information from a mouse model, a strong spectral correlation was also recognized with the blood glucose concentration. The glucose fingerprint information detected from the nailfolds of 30 human volunteers exhibited concentration dependent changes, especially when the intraspectrum intensity ratio was calculated between 1125 cm-1 and 1445 cm-1 to monitor normalized differences in the glucose Raman band. Furthermore, by accounting for all intersubject variations observed in the acquired spectral features, a particle swarm optimization-backpropagation artificial neural network (PSO-BP-ANN) model was proposed for linking measured Raman information with actual glucose concentrations quantitatively. Following model training and testing, the prediction accuracy of the PSO-BP-ANN model was evaluated using 12 spectra acquired from an additional three volunteers. Statistical evaluations indicated that the proposed methodology may have a good application potential for in vivo transcutaneous spectral glucose monitoring.

Emission Integral Effect on Non-Invasive Blood Glucose Measurements Made Using Mid-Infrared Passive Spectroscopic Imaging

Living bodies emit mid-infrared light (wavelength band centered at approximately 10 µm) with a temperature-dependent intensity. Several studies have shown the possibility of measuring blood glucose levels using the mid-infrared emission of living bodies, and we have demonstrated non-invasive blood glucose measurements through distant wrist measurements (wavelength 8-14 µm) by mid-infrared passive spectroscopic imaging. However, it is not clear why blood glucose is detectable, as there is no formula that shows the effect of material thickness and concentration on emission intensity. In this study, we developed a principle for understanding glucose detection by proposing that an emission integral effect underpins the changes in emission intensity with substance thickness and absorption coefficient. We demonstrate the emission integral effect by measuring the spectral radiance of polypropylene with different thicknesses using mid-infrared passive spectroscopic imaging. The simulation results based on the emission integral effect indicate that in living bodies, dilute components such as glucose are easier to identify than components with high concentrations. Mid-infrared passive spectroscopic imaging offers potential innovative solutions for measuring various substances from a distance, with the emission integral effect acting as the basic working principle.

A comprehensive review of non-invasive optical and microwave biosensors for glucose monitoring

Frequent glucose monitoring is essential for effective diabetes management. Currently, glucose monitoring is done using invasive methods such as finger-pricking and subcutaneous sensing. However, these methods can cause discomfort, heighten the risk of infection, and some sensing devices need frequent calibration. Non-invasive glucose monitoring technologies have attracted significant attention due to their potential to overcome the limitations of their invasive counterparts by offering painless and convenient alternatives. This review focuses on two prominent approaches to non-invasive glucose sensing: optical- and microwave-based methods. On one hand, optical techniques, including Raman and near-infrared (NIR) spectroscopy, leverage the unique spectral properties of glucose molecules to measure their concentration in tissues and biofluids. On the other hand, microwave sensing leverages the dielectric properties of glucose to detect concentration changes based on impedance measurements. Despite their promise, optical- and microwave-based technologies face challenges such as signal interference and high variability due to tissue heterogeneity, which impact their accuracy and reliability. This review provides a comprehensive overview of the advancements of these non-invasive methods, highlighting their technical implementation, limitations, and their future potential in revolutionizing glucose monitoring for diabetes care.

Enzymatic properties of iron oxide nanoclusters and their application as a colorimetric glucose detection probe

Nanozymes have attracted attention owing to their distinct catalytic capabilities and potential applications, being advantageous compared to natural enzymes in terms of storage and cost efficiency. In this study, we investigated the enzymatic properties of iron oxide nanoclusters (IOCs) formed through the clustering of small nanoparticles. Our findings reveal that the enzymatic activity of IOCs is enhanced as their size increases. Additionally, we demonstrated that the size of the unit particles within IOCs is highly dependent on the nucleation environment, which is a crucial factor in determining the overall size of the IOCs. Importantly, the surface area of IOCs is more closely related to the size of the individual unit particles rather than the entire cluster. Smaller unit particle sizes within IOCs resulted in an increase in nanocluster size, thereby augmenting the specific surface area. The optimal IOC exhibited superior stability under various conditions and a broader range of reactivity compared to natural enzymes, making it a promising probe material for point-of-care tests across diverse environments. Furthermore, its effectiveness as a glucose detection probe was demonstrated, highlighting its potential for practical applications. The remarkable enzyme-like efficacy of IOCs not only enhances their utility in on-site detection technologies but also establishes them as a versatile detection probe.

Subcutaneous depth-selective spectral imaging with mμSORS enables noninvasive glucose monitoring

Noninvasive blood glucose monitoring offers substantial advantages for patients, but current technologies are often not sufficiently accurate for clinical applications or require personalized calibration. Here we report multiple μ-spatially offset Raman spectroscopy, which captures Raman signals at varying skin depths, and show that it accurately detects blood glucose levels in humans. In 35 individuals with or without type 2 diabetes, we first determine the optimal depth for glucose detection to be at or below the capillary-rich dermal-epidermal junction, where we observe a strong correlation between specific Raman bands and venous plasma glucose concentrations. In a second study, comprising 230 participants, we then improve accuracy of our regression model to reach a mean absolute relative difference of 14.6%, without personalized calibration, whereby 99.4% of calculated glucose values fall into clinically acceptable zones of the consensus error grid (zones A and B). These findings highlight the ability and robustness of multiple μ-spatially offset Raman spectroscopy for noninvasive blood glucose measurement in a clinical setting.

Glucose Sensor Design Based on Monte Carlo Simulation

Continuous glucose monitoring based on the minimally invasive implantation of glucose sensor is characterized by high accuracy and good stability. At present, glucose concentration monitoring based on fluorescent glucose capsule sensor is a new development trend. In this paper, we design a fluorescent glucose capsule sensor with a design optimization study. The motion trajectory of incident light in the fluorescent gel layer is simulated based on the Monte Carlo method, and the cloud maps of light intensity with the light intensity distribution at the light-receiving layer are plotted. Altering the density of fluorescent molecules, varying the thickness of tissue layers, and adjusting the angle of incidence deflection, the study investigates the influence of these parameter changes on the optimal position of reflected light at the bottom. Finally, the simulation results were utilized to design and fabricate a fluorescent glucose capsule sensor. Rabbit subcutaneous tissue glucose level tests and real-time glucose solution concentration monitoring experiments were performed. This work contributes to the real-time monitoring of glucose levels and opens up new avenues for research on fabricating glucose sensors.

Evaluating the precision and reliability of real-time continuous glucose monitoring systems in ambulatory settings: a systematic review

Background

Continuous glucose monitoring (CGM) with minimally invasive devices plays a key role in the assessment of daily diabetes management by detecting and alerting to potentially dangerous trends in glucose levels, improving quality of life, and treatment adherence. However, there is still uncertainty as to whether CGMs are accurate enough to replace self-monitoring of blood glucose, especially in detecting episodes of hypoglycemia.

Objectives

Evaluate clinical, numerical accuracy, sensitivity, and specificity of the CGM devices commercially available when compared to the reference standard of arterial or venous blood glucose.

Data sources and methods

We searched the Cochrane Library, PubMed, EMBASE, and LILACS databases. The quality was assessed with the Quality Assessment Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical and numerical accuracy data were extracted. Sensitivity and specificity were calculated using Review Manager software. Heterogeneity was assessed by visual examination of forest plot and summary receiver operating characteristic curves.

Results

Twenty-two studies with a total of 2294 patients were included. The average mean absolute relative difference for overall diagnostic accuracy was 9.4%. None of the devices evaluated with ISO 15197:2013 criteria achieved values ⩾95% of measurements in the stipulated ranges in hypoglycemia (±15 mg/dL), but two devices did achieve it in hyperglycemia (±15%; Dexcom G6 and G7). Most of the devices evaluated with consensus error grids reached values above 99% in zones A and B only in overall accuracy and hyperglycemia. For hypoglycemia, the average sensitivity was 85.7% and specificity 95.33%, and for hyperglycemia was 97.45% and 96% respectively.

Conclusion

Currently available CGM devices have adequate accuracy for euglycemia and hyperglycemia; however, it is still inadequate for hypoglycemia, although it has improved over time.

Trial registration

Prospero registration ID CRD42023399767.

Performance of a Non-Invasive System for Monitoring Blood Glucose Levels Based on Near-Infrared Spectroscopy Technology (Glucube®)

BACKGROUND

The need for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG (SMBG) have driven the development of non-invasive approaches.

METHODS

This prospective study aimed to investigate the accuracy of glucose level calculation using the near-infrared spectroscopy (NIRS) technology Glucube® system. People with Type 1 diabetes, Type 2 diabetes, prediabetes, and normal glucose metabolism were included. Over one week, individuals performed glucose measurements with the Glucube® system and capillary blood fingersticks with a standard glucometer (Ascensia Contour® Next). To assess the impact of the improvement in dexterity, the accuracy variables were compared with the point-of-care (POC) glucometer Accu-Chek® Inform II in a one-week sub-study.

RESULTS

Overall, 105 subjects (mean age 53.8 ± 13.8 years, 50.5% female) participated, resulting in 1914 paired glucose measurements between 49 and 331 mg/dL. Total mean absolute relative difference (MARD) was 20.3%, MARD for values >100 mg/dL was 18.3%, and mean absolute deviation (MAD) for values <100 mg/dL was 24.9%. A total of 97.3% of measurements fell within A+B Parkes zones, and 58.8%, 76.9%, and 88.1% within +-20%, +-30%, or +-40% error, respectively. On completion, 62 participants (59%) fulfilled the one-week prospective sub-study. In this subgroup, the total MARD was reduced between day 1 and day 8 from 22.8 to 18.3% (p = 0.068). The percentages within Zone A were 51.6 vs. 61.2%, Zone B 46.8 vs. 33.9%, and Zone C 1.6 vs. 4.8%, and the sum of Parkes Zones A+B was 98.4 vs. 95.2% (p = 0.311) for day 1 and day 8, respectively.

CONCLUSIONS

Glucube® is a novel non-invasive system based on NIRS technology for monitoring blood glucose levels. Its promising capabilities support further research.

Technological advancements in glucose monitoring and artificial pancreas systems for shaping diabetes care

The management of diabetes mellitus has undergone remarkable progress with the introduction of cutting-edge technologies in glucose monitoring and artificial pancreas systems. These innovations have revolutionized diabetes care, offering patients more precise, convenient, and personalized management solutions that significantly improve their quality of life. This review aims to provide a comprehensive overview of recent technological advancements in glucose monitoring devices and artificial pancreas systems, focusing on their transformative impact on diabetes care. A detailed review of the literature was conducted to examine the evolution of glucose monitoring technologies, from traditional invasive methods to more advanced systems. The review explores minimally invasive techniques such as continuous glucose monitoring (CGM) systems and flash glucose monitoring (FGM) systems, which have already been proven to enhance glycemic control and reduce the risk of hypoglycemia. In addition, emerging non-invasive glucose monitoring technologies, including optical, electrochemical, and electro-mechanical methods, were evaluated. These techniques are paving the way for more patient-friendly options that eliminate the need for frequent finger-prick tests, thereby improving adherence and ease of use. Advancements in closed-loop artificial pancreas systems, which integrate CGM with automated insulin delivery, were also examined. These systems, often referred to as "hybrid closed-loop" or "automated insulin delivery" systems, represent a significant leap forward in diabetes care by automating the process of insulin dosing. Such advancements aim to mimic the natural function of the pancreas, allowing for better glucose regulation without the constant need for manual interventions by the patient. Technological breakthroughs in glucose monitoring and artificial pancreas systems have had a profound impact on diabetes management, providing patients with more accurate, reliable, and individualized treatment options. These innovations hold the potential to significantly improve glycemic control, reduce the incidence of diabetes-related complications, and ultimately enhance the quality of life for individuals living with diabetes. Researchers are continually exploring novel methods to measure glucose more effectively and with greater convenience, further refining the future of diabetes care. Researchers are also investigating the integration of artificial intelligence and machine learning algorithms to further enhance the precision and predictive capabilities of glucose monitoring and insulin delivery systems. With ongoing advancements in sensor technology, connectivity, and data analytics, the future of diabetes care promises to deliver even more seamless, real-time management, empowering patients with greater autonomy and improved health outcomes.

Engineering with Biomedical Sciences Changing the Horizon of Healthcare-A Review

In the dynamic realm of healthcare, the convergence of engineering and biomedical sciences has emerged as a pivotal frontier. In this review we go into specific areas of innovation, including medical imaging and diagnosis, developments in biomedical sensors, and drug delivery systems. Wearable biosensors, non-wearable biosensors, and biochips, which include gene chips, protein chips, and cell chips, are all included in the scope of the topic that pertains to biomedical sensors. Extensive research is conducted on drug delivery systems, spanning topics such as the integration of computer modeling, the optimization of drug formulations, and the design of delivery devices. Furthermore, the paper investigates intelligent drug delivery methods, which encompass stimuli-responsive systems such as temperature, redox, pH, light, enzyme, and magnetic responsive systems. In addition to that, the review goes into topics such as tissue engineering, regenerative medicine, biomedical robotics, automation, biomechanics, and the utilization of green biomaterials. The purpose of this analysis is to provide insights that will enhance continuing research and development efforts in engineering-driven biomedical breakthroughs, ultimately contributing to the improvement of healthcare. These insights will be provided by addressing difficulties and highlighting future prospects.

Patent Overview of Innovative Hyaluronic Acid-Based Hydrogel Biosensors

Hyaluronic acid-based hydrogels are emerging as highly versatile materials for cost-effective biosensors, capable of sensitive chemical and biological detection. These hydrogels, functionalized with specific groups, exhibit sensitivity modulated by factors such as temperature, pH, and analyte concentration, allowing for a broad spectrum of applications. This study presents a patent-centered overview of recent advancements in hyaluronic acid hydrogel biosensors from 2003 to 2023. A total of 50 patent documents-including 41 patent applications and 9 granted patents-reveal a growing interest, primarily driven by United States-based institutions, which account for approximately 54% of all filings. This trend reflects the strong collaboration between universities, industry, and foundations in pushing this technology forward. Most patented technologies focus on biosensors for in vivo blood analysis, measuring critical parameters such as gas concentration and pH, with particular emphasis on glucose monitoring via tissue impedance using enzyme-immobilized oxidase electrodes. Additionally, the 9 granted patents collectively showcase key innovations, highlighting applications from continuous glucose monitors to implantable vascular devices and sweat analyte detection systems. These patents underscore the adaptability and biocompatibility of hyaluronic acid hydrogels, reinforcing their role in enhancing biosensor performance for real-time health monitoring. In summary, this overview highlights the importance of patent analysis in tracking and directing research and development, helping to clarify the field's evolution and identify innovation gaps for hyaluronic acid-based hydrogel biosensors.

Microfluidic-based systems for the management of diabetes

Diabetes currently affects approximately 500 million people worldwide and is one of the most common causes of mortality in the United States. To diagnose and monitor diabetes, finger-prick blood glucose testing has long been used as the clinical gold standard. For diabetes treatment, insulin is typically delivered subcutaneously through cannula-based syringes, pens, or pumps in almost all type 1 diabetic (T1D) patients and some type 2 diabetic (T2D) patients. These painful, invasive approaches can cause non-adherence to glucose testing and insulin therapy. To address these problems, researchers have developed miniaturized blood glucose testing devices as well as microfluidic platforms for non-invasive glucose testing through other body fluids. In addition, glycated hemoglobin (HbA1c), insulin levels, and cellular biomechanics-related metrics have also been considered for microfluidic-based diabetes diagnosis. For the treatment of diabetes, insulin has been delivered transdermally through microdevices, mostly through microneedle array-based, minimally invasive injections. Researchers have also developed microfluidic platforms for oral, intraperitoneal, and inhalation-based delivery of insulin. For T2D patients, metformin, glucagon-like peptide 1 (GLP-1), and GLP-1 receptor agonists have also been delivered using microfluidic technologies. Thus far, clinical studies have been widely performed on microfluidic-based diabetes monitoring, especially glucose sensing, yet technologies for the delivery of insulin and other drugs to diabetic patients with microfluidics are still mostly in the preclinical stage. This article provides a concise review of the role of microfluidic devices in the diagnosis and monitoring of diabetes, as well as the delivery of pharmaceuticals to treat diabetes using microfluidic technologies in the recent literature.

Photoacoustic Resonators for Non-Invasive Blood Glucose Detection Through Photoacoustic Spectroscopy: A Systematic Review

Diabetes mellitus is a prevalent disease with a rapidly increasing incidence projected worldwide, affecting both industrialized and developing regions. Effective diabetes management requires precise therapeutic strategies, primarily through self-monitoring of blood glucose levels to achieve tight glycemic control, thereby mitigating the risk of severe complications. In recent years, there have been significant advancements in non-invasive techniques for measuring blood glucose using photoacoustic spectroscopy (PAS), as it shows great promise for the detection of glucose using the infrared region (e.g., MIR and NIR) of light. A critical aspect of this method is the detection of the photoacoustic signal generated from blood glucose, which needs to be amplified through a photoacoustic resonator (PAR). In this work, an overview of various types of PARs used for non-invasive glucose sensing is reviewed, highlighting their operating principle, design requirements, limitations, and potential improvements needed to enhance the analysis of photoacoustic signals. The motivation behind this review is to identify and discuss main parameters crucial to the efficient design of PARs used in non-invasive glucose detection, which will be helpful for furthering the basic understanding of this technology and achieving the highly sensitive PAR required for non-invasive glucose monitoring.

Artificial Intelligence-Based Digital Biomarkers for Type 2 Diabetes: A Review

Type 2 diabetes mellitus (T2DM), a complex metabolic disorder that burdens the health care system, requires early detection and treatment. Recent strides in digital health technologies, coupled with artificial intelligence (AI), may have the potential to revolutionize T2DM screening, diagnosis of complications, and management through the development of digital biomarkers. This review provides an overview of the potential applications of AI-driven biomarkers in the context of screening, diagnosing complications, and managing patients with T2DM. The benefits of using multisensor devices to develop digital biomarkers are discussed. The summary of these findings and patterns between model architecture and sensor type are presented. In addition, we highlight the pivotal role of AI techniques in clinical intervention and implementation, encompassing clinical decision support systems, telemedicine interventions, and population health initiatives. Challenges such as data privacy, algorithm interpretability, and regulatory considerations are also highlighted, alongside future research directions to explore the use of AI-driven digital biomarkers in T2DM screening and management.

Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

A comprehensive review on electromagnetic wave based non-invasive glucose monitoring in microwave frequencies

Diabetes is a chronic disease that affects millions of humans worldwide. This review article provides an analysis of the recent advancements in non-invasive blood glucose monitoring, detailing methods and techniques, with a special focus on Electromagnetic wave microwave glucose sensors. While optical, thermal, and electromagnetic techniques have been discussed, the primary emphasis is focussed on microwave frequency sensors due to their distinct advantages. Microwave sensors exhibit rapid response times, require minimal user intervention, and hold potential for continuous monitoring, renders them extremely potential for real-world applications. Additionally, their reduced susceptibility to physiological interferences further enhances their appeal. This review critically assesses the performance of microwave glucose sensors by considering factors such as accuracy, sensitivity, specificity, and user comfort. Moreover, it sheds light on the challenges and upcoming directions in the growth of microwave sensors, including the need for reduction and integration with wearable platforms. By concentrating on microwave sensors within the broader context of non-invasive glucose monitoring, this article aims to offer significant enlightenment that may drive further innovation in diabetes care.

Calibration algorithms for continuous glucose monitoring systems based on interstitial fluid sensing

Continuous glucose monitoring (CGM) is of great importance to the treatment and prevention of diabetes. As a proven commercial technology, electrochemical glucose sensor based on interstitial fluid (ISF) sensing has high sensitivity and wide detection range. Therefore, it has good promotion prospects in noninvasive or minimally-invasive CGM system. However, since there are concentration differences and time lag between glucose in plasma and ISF, the accuracy of this type of sensors are still limited. Typical calibration algorithms rely on simple linear regression which do not account for the variability of the sensitivity of sensors. To enhance the accuracy and stability of CGM based on ISF, optimization of calibration algorithm for sensors is indispensable. While there have been considerable researches on improving calibration algorithms for CGM, they have still received less attention. This article reviews the problem of typical calibration and presents the outstanding calibration algorithms in recent years. Finally, combined with existing research and emerging sensing technologies, this paper makes an outlook on the future calibration algorithms for CGM sensors.

Optical blood glucose non-invasive detection and its research progress

Blood glucose concentration is an important index for the diagnosis of diabetes, its self-monitoring technology is the method for scientific diabetes management. Currently, the typical household blood glucose meters have achieved great success in diabetes management, but they are discrete detection methods, and involve invasive blood sampling procedures. Optical detection technologies, which use the physical properties of light to detect the glucose concentration in body fluids non-invasively, have shown great potential in non-invasive blood glucose detection. This article summarized and analyzed the basic principles, research status, existing problems, and application prospects of different optical glucose detection technologies. In addition, this article also discusses the problems of optical detection technology in wearable sensors and perspectives on the future of non-invasive blood glucose detection technology to improve blood glucose monitoring in diabetic patients.

Artificial intelligence for diabetes care: current and future prospects

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

A self-powered and supercapacitive microneedle continuous glucose monitoring system with a wide range of glucose detection capabilities

Continuous detection of sudden changes in blood glucose is essential for individuals with diabetes who have difficulty in maintaining optimal control of their blood glucose levels. Hypoglycemic shock or a hyperglycemic crisis are likely to occurs in patients with diabetes and poses a significant threat to their lives. Currently, commercial continuous glucose monitoring (CGM) has limits in the glucose concentration detection range, which is 40-500 mg/dL, making it difficult to prevent the risk of hyperglycemic shock. In addition, current CGMs are invasive, cause pain and irritation during usage, and expensive. In this research, we overcome these limitations by introducing a novel mechanism to detect glucose concentration using supercapacitors. The developed CGM, which is self-powered and minimally invasive due to the use of microneedles, can detect a wider range of glucose concentrations than commercial sensors. In addition, efficacy and stability were proven through in vitro and in vivo experiments. Thus, this self-powered, microneedle and supercapacitive-type CGM can potentially prevent both hypoglycemic and complications of hyperglycemia without pain and with less power consumption than current commercial sensors.

Advances in Nanomedicine for Precision Insulin Delivery

Diabetes mellitus, which comprises a group of metabolic disorders affecting carbohydrate metabolism, is characterized by improper glucose utilization and excessive production, leading to hyperglycemia. The global prevalence of diabetes is rising, with projections indicating it will affect 783.2 million people by 2045. Insulin treatment is crucial, especially for type 1 diabetes, due to the lack of β-cell function. Intensive insulin therapy, involving multiple daily injections or continuous subcutaneous insulin infusion, has proven effective in reducing microvascular complications but poses a higher risk of severe hypoglycemia. Recent advancements in insulin formulations and delivery methods, such as ultra-rapid-acting analogs and inhaled insulin, offer potential benefits in terms of reducing hypoglycemia and improving glycemic control. However, the traditional subcutaneous injection method has drawbacks, including patient compliance issues and associated complications. Nanomedicine presents innovative solutions to these challenges, offering promising avenues for overcoming current drug limitations, enhancing cellular uptake, and improving pharmacokinetics and pharmacodynamics. Various nanocarriers, including liposomes, chitosan, and PLGA, provide protection against enzymatic degradation, improving drug stability and controlled release. These nanocarriers offer unique advantages, ranging from enhanced bioavailability and sustained release to specific targeting capabilities. While oral insulin delivery is being explored for better patient adherence and cost-effectiveness, other nanomedicine-based methods also show promise in improving delivery efficiency and patient outcomes. Safety concerns, including potential toxicity and immunogenicity issues, must be addressed, with the FDA providing guidance for the safe development of nanotechnology-based products. Future directions in nanomedicine will focus on creating next-generation nanocarriers with precise targeting, real-time monitoring, and stimuli-responsive features to optimize diabetes treatment outcomes and patient safety. This review delves into the current state of nanomedicine for insulin delivery, examining various types of nanocarriers and their mechanisms of action, and discussing the challenges and future directions in developing safe and effective nanomedicine-based therapies for diabetes management.

Equivalent Electrical Circuit Approach to Enhance a Transducer for Insulin Bioavailability Assessment

The equivalent electrical circuit approach is explored to improve a bioimpedance-based transducer for measuring the bioavailability of synthetic insulin already presented in previous studies. In particular, the electrical parameter most sensitive to the variation of insulin amount injected was identified. Eggplants were used to emulate human electrical behavior under a quasi-static assumption guaranteed by a very low measurement time compared to the estimated insulin absorption time. Measurements were conducted with the EVAL-AD5940BIOZ by applying a sinusoidal voltage signal with an amplitude of 100 mV and acquiring impedance spectra in the range [1-100] kHz. 14 units of insulin were gradually administered using a Lilly's Insulin Pen having a 0.4 cm long needle. Modified Hayden's model was adopted as a reference circuit and the electrical component modeling the extracellular fluids was found to be the most insulin-sensitive parameter. The trnasducer achieves a state-of-the-art sensitivity of 225.90 ml1. An improvement of 223 % in sensitivity, 44 % in deterministic error, 7 % in nonlinearity, and 42 % in reproducibility was achieved compared to previous experimental studies. The clinical impact of the transducer was evaluated by projecting its impact on a Smart Insulin Pen for real-time measurement of insulin bioavailability. The wide gain in sensitivity of the bioimpedance-based transducer results in a significant reduction of the uncertainty of the Smart Insulin Pen. Considering the same improvement in in-vivo applications, the uncertainty of the Smart Insulin Pen is decreased from [Formula: see text]l to [Formula: see text]l.Clinical and Translational Impact Statement: A Smart Insulin Pen based on impedance spectroscopy and equivalent electrical circuit approach could be an effective solution for the non-invasive and real-time measurement of synthetic insulin uptake after subcutaneous administration.

DEMIGOD: A Low-Cost Microcontroller-Based Closed-Loop System Integrating Nanoengineered Sweat-Based Glucose Monitoring and Controlled Transdermal Nanoemulsion Release of Hypoglycemic Treatment with a Software Application for Noninvasive Personalized Diabetes Care

This study endeavored to design and develop an innovative closed-loop diagnostic and therapeutic system with the following objectives: (a) the noninvasive detection of glucose concentration in sweat utilizing nanonengineered screen-printed biosensors; (b) the management of measured data through a specialized computer system comprising both hardware and software components, thereby enabling the precise control of therapeutic responses via a patch-based nanomedicine delivery system. This initiative addresses the significant challenges inherent in the management of diabetes mellitus, including the imperative need for glucose-level monitoring to optimize glycemic control. Leveraging chronoamperometric results as a foundational dataset and the in vivo hypoglycemic activity of nanoemulsion formulations, this research underscores the efficacy and accuracy of glucose concentration estimation, decision-making mechanism responses, and transdermal hypoglycemic treatment effects, within the proposed system.

Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and ECG Signal

This paper presents a novel approach to noninvasive hyperglycemia monitoring utilizing electrocardiograms (ECG) from an extensive database comprising 1119 subjects. Previous research on hyperglycemia or glucose detection using ECG has been constrained by challenges related to generalization and scalability, primarily due to using all subjects' ECG in training without considering unseen subjects-a critical factor for developing methods with effective generalization. We designed a deep neural network model capable of identifying significant features in various spatial locations and examining the interdependencies between different features within each convolutional layer. To accelerate processing speed, we segment the ECG of each user to isolate one heartbeat or one cycle of the ECG. Our model was trained using data from 727 subjects, while 168 were used for validation. The testing phase involved 224 unseen subjects, with a dataset consisting of 9,000 segments. The result indicates that the proposed algorithm effectively detects hyperglycemia with a curve area of 93.05% (AUC), a sensitivity of 83.46%, and a specificity of 86.04%.

Wrist measurement for millimeter-wave non-invasive glucose monitoring: Numerical analysis of an anatomically realistic tissue model

This paper shows the advantages of wrist measurement for millimeter-wave (MMW) non-invasive glucose monitoring based on an anatomically realistic tissue model. The wrist possesses not only its good accessibility but also its thin epidermis and the existence of a radial artery, which enable MMW to acquire blood signals with a high sensitivity. We analyze their scattering characteristics according to blood glucose change in a frequency range of 60-90 GHz. Analysis results indicate that MMW senses skin microvasculature and radial artery, leading to high sensitivity to blood glucose levels. This work contributes to achieving reliable non-invasive blood glucose monitoring.

Improved glycaemic control induced by evening activity breaks does not persist overnight amongst healthy adults: A randomized crossover trial

AIMS

To compare the effects of 4 hours of laboratory-based regular activity breaks (RABs) and prolonged sitting (SIT) on subsequent 48-h free-living interstitial glucose levels in a group of healthy adults.

MATERIALS AND METHODS

In this randomized crossover trial, participants completed two 4-h laboratory-based interventions commencing at ~5:00 pm: (1) SIT and (2) SIT interrupted with 3 min of body weight resistance exercise activity breaks every 30 min (RABs). Continuous glucose monitoring was performed during the intervention and for 48-h after, during which time participants returned to a free-living setting.

RESULTS

Twenty-eight adults (female n = 20, mean ± SD age 25.5 ± 5.6 years, body mass index 29.2 ± 6.9 kg/m2) provided data for this analysis. During the intervention period, RABs lowered mean interstitial glucose by 8.3% (-0.47 mmol/L/4 h, 95% confidence interval [CI] -0.74 to -0.20; p = 0.001) and area under the curve (AUC) by 8.9% (-2.01 mmol/L/4 h, 95% CI -3.05 to -0.97; p < 0.001) compared to SIT. Measures of glycaemic variability were not significantly different during the intervention. There were no significant differences in mean glucose and AUC between conditions during the first nocturnal period and 24-h post intervention. When compared to SIT, RABs increased continuous overall net action of glucose at 1 h and SD glucose by 22% (0.18 mmol/L, 95% CI 0.03 to 0.29; p = 0.018) and 26% (95% CI 4.9 to 42.7; p = 0.019) in the first nocturnal period and by 10% (0.09 mmol/L, 95% CI 0.01, 0.17; p = 0.025) and 15% (95% CI 6.6 to 22.4; p = 0.001) in the 24-h post intervention period, respectively.

CONCLUSION

Performing activity breaks in the evening results in acute reductions in interstitial glucose concentrations; however, the magnitude of these changes is not maintained overnight or into the following 48 hours.

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

Fabrication of Multiple-Channel Electrochemical Microneedle Electrode Array via Separated Functionalization and Assembly Method

Real-time monitoring of physiological indicators inside the body is pivotal for contemporary diagnostics and treatments. Implantable electrodes can not only track specific biomarkers but also facilitate therapeutic interventions. By modifying biometric components, implantable electrodes enable in situ metabolite detection in living tissues, notably beneficial in invasive glucose monitoring, which effectively alleviates the self-blood-glucose-managing burden for patients. However, the development of implantable electrochemical electrodes, especially multi-channel sensing devices, still faces challenges: (1) The complexity of direct preparation hinders functionalized or multi-parameter sensing on a small scale. (2) The fine structure of individual electrodes results in low spatial resolution for sensor functionalization. (3) There is limited conductivity due to simple device structures and weakly conductive electrode materials (such as silicon or polymers). To address these challenges, we developed multiple-channel electrochemical microneedle electrode arrays (MCEMEAs) via a separated functionalization and assembly process. Two-dimensional microneedle (2dMN)-based and one-dimensional microneedle (1dMN)-based electrodes were prepared by laser patterning, which were then modified as sensing electrodes by electrochemical deposition and glucose oxidase decoration to achieve separated functionalization and reduce mutual interference. The electrodes were then assembled into 2dMN- and 1dMN-based multi-channel electrochemical arrays (MCEAs), respectively, to avoid damaging functionalized coatings. In vitro and in vivo results demonstrated that the as-prepared MCEAs exhibit excellent transdermal capability, detection sensitivity, selectivity, and reproducibility, which was capable of real-time, in situ glucose concentration monitoring.

Mechanical Robustness Enhanced Flexible Antennas Using Ti3C2 MXene and Nanocellulose Composites for Noninvasive Glucose Sensing

Electromagnetic sensors with flexible antennas as sensing elements have attracted increasing attention in noninvasive continuous glucose monitoring for diabetic patients. The significant radiation performance loss of flexible antennas during mechanical deformation impairs the reliability of glucose monitoring. Here, we present flexible ultrawideband monopole antennas composed of Ti3C2 MXene and cellulose nanofibril (CNF) composite films for continuous glucose monitoring. The flexible MXene/CNF antenna with 20% CNF content can obtain a gain of up to 3.33 dBi and a radiation efficiency of up to 65.40% at a frequency range from 2.3 to 6.0 GHz. Compared with the pure MXene antenna, this antenna offers a comparable radiation performance and a lower performance loss in mechanical bending deformation. Moreover, the MXene/CNF antenna shows a stable response to fetal bovine serum/glucose, with a correlation of >0.9 at the reference glucose levels, and responds sensitively to the variations in blood glucose levels during human trials. The proposed strategy enhancing the mechanical robustness of MXene-based flexible antennas makes metallic two-dimensional nanomaterials more promising in wearable electromagnetic sensors.

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

Periodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900-1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed 'niGLUC-2.0v'. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland-Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.

Non-invasive glucose prediction and classification using NIR technology with machine learning

In this paper, a dual wavelength short near-infrared system is described for the detection of glucose levels. The system aims to improve the accuracy of blood glucose detection in a cost-effective and non-invasive way. The accuracy of the method is evaluated using real-time samples collected with the reference finger prick glucose device. A feed forward neural network (FFNN) regression method is employed to predict glucose levels based on the input data obtained from NIR technology. The system calculates glucose evaluation metrics and performs Surveillance error grid (SEG) analysis. The coefficient of determination R 2 and mean absolute error are observed 0.99 and 2.49 mg/dl, respectively. Additionally, the system determines the root mean square error (RMSE) as 3.02 mg/dl. It also shows that the mean absolute percentage error (MAPE) is 1.94% and mean squared error (MSE) is 9.16 ( m g / d l ) 2 for FFNN. The SEG analysis shows that the glucose values measured by the system fall within the clinically acceptable range when compared to the reference method. Finally, the system uses the multi-class classification method of the multilayer perceptron (MLP) and K-nearest neighbors (KNN) classifier to classify glucose levels with an accuracy of 99%.

Recent Insights into Glucose-Responsive Concanavalin A-Based Smart Hydrogels for Controlled Insulin Delivery

Many efforts are continuously undertaken to develop glucose-sensitive biomaterials able of controlling glucose levels in the body and self-regulating insulin delivery. Hydrogels that swell or shrink as a function of the environmental free glucose content are suitable systems for monitoring blood glucose, delivering insulin doses adapted to the glucose concentration. In this context, the development of sensors based on reversible binding to glucose molecules represents a continuous challenge. Concanavalin A (Con A) is a bioactive protein isolated from sword bean plants (Canavalia ensiformis) and contains four sugar-binding sites. The high affinity for reversibly and specifically binding glucose and mannose makes Con A as a suitable natural receptor for the development of smart glucose-responsive materials. During the last few years, Con A was used to develop smart materials, such as hydrogels, microgels, nanoparticles and films, for producing glucose biosensors or drug delivery devices. This review is focused on Con A-based materials suitable in the diagnosis and therapeutics of diabetes. A brief outlook on glucose-derived theranostics of cancer is also presented.

Evolution of Minimally Invasive and Non-Invasive Preimplantation Genetic Testing: An Overview

Preimplantation genetic testing (PGT) has become a common supplementary diagnοstic/testing tοol for in vitro fertilization (ΙVF) cycles due to a significant increase in cases of PGT fοr mοnogenic cοnditions (ΡGT-M) and de novο aneuplοidies (ΡGT-A) over the last ten years. This tendency is mostly attributable to the advancement and application of novel cytogenetic and molecular techniques in clinical practice that are capable of providing an efficient evaluation of the embryonic chromosomal complement and leading to better IVF/ICSI results. Although PGT is widely used, it requires invasive biopsy of the blastocyst, which may harm the embryo. Non-invasive approaches, like cell-free DNA (cfDNA) testing, have lower risks but have drawbacks in consistency and sensitivity. This review discusses new developments and opportunities in the field of preimplantation genetic testing, enhancing the overall effectiveness and accessibility of preimplantation testing in the framework of developments in genomic sequencing, bioinformatics, and the integration of artificial intelligence in the interpretation of genetic data.

Gold nanoparticle-powered screening of membrane protein-specific lipids from complex lipid mixtures

Membrane proteins (MPs) are affected by binding of specific lipids. We previously developed a methodology for systematically analyzing MP-lipid interactions leveraging surface plasmon resonance (SPR). In this method, the gold sensor chip surface was modified with a self-assembled monolayer (SAM), which allowed for a larger amount of MP-immobilization. However, the laborious lipid purification step remained a bottleneck. To address this issue, a new strategy has been developed utilizing gold nanoparticles (AuNPs) instead of the gold sensor chip. AuNPs were coated with SAM, on which MP was covalently anchored. The MP-immobilized AuNPs were mixed with a lipid mixture, and the recovered lipids were quantified by LC-MS. Bacteriorhodopsin (bR) was used as an MP to demonstrate this concept. We optimized immobilization conditions and confirmed the efficient immobilization of bR by dynamic light scattering and electron micrographs. Washing conditions for pulldown experiments were optimized to efficiently remove non-specific lipids. A new binding index was introduced to qualitatively reproduce the known affinity of lipids for bR. Consequently, the low-abundant and least-studied lipid S-TeGD was identified as a candidate for bR-specific lipids. This technique can skip the laborious lipid purification process, accelerating the screening of MP-specific lipids from complex lipid mixtures.

Non-invasive measurements of blood glucose levels by time-gating mid-infrared optoacoustic signals

Non-invasive glucose monitoring (NIGM) represents an attractive alternative to finger pricking for blood glucose assessment and management of diabetes. Nevertheless, current NIGM techniques do not measure glucose concentrations in blood but rely on indirect bulk measurement of glucose in interstitial fluid, where glucose is diluted and glucose dynamics are different from those in the blood, which impairs NIGM accuracy. Here we introduce a new biosensor, termed depth-gated mid-infrared optoacoustic sensor (DIROS), which allows, for the first time, non-invasive glucose detection in blood-rich volumes in the skin. DIROS minimizes interference caused by the stratum corneum and other superficial skin layers by time-gating mid-infrared optoacoustic signals to enable depth-selective localization of glucose readings in skin. In measurements on the ears of (female) mice, DIROS displays improved accuracy over bulk-tissue glucose measurements. Our work demonstrates how signal localization can improve NIGM accuracy and positions DIROS as a holistic approach, with high translational potential, that addresses a key limitation of current NIGM methods.